The lag and cumulative response of water use efficiency to the climate on the Shiyang River Basin, Northwest of China

doi:10.21203/rs.3.rs-4958542/v1

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The lag and cumulative response of water use efficiency to the climate on the Shiyang River Basin, Northwest of China

https://doi.org/10.21203/rs.3.rs-4958542/v1

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Water use efficiency (WUE), as an indicator for plants to regulate water physiological processes through photosynthesis, is a key link between carbon and water cycling in ecosystems, reflecting the rapid adaptation strategies of vegetation ecosystems to site environment and resource changes. In this study, satellite data and ground-based observation data from 2001–2020 were developed to simulate and estimate the spatial distribution characteristics of WUE in different functional zones and analyze the time lag and cumulative effects of climate on vegetation. The results show that: (1) In the past 20 years, the spatial distribution of WUE in the SRB showed a south-high, north-low, with high-value areas primarily in the southern part of Zone I, Zone II, the Changning Oasis and Jinchuan in Zone Ⅲ, and the Hexi Bao Oasis. Temporal analysis within the SRB indicated a decreasing trend in WUE across different ecological functional zones, notably most significant in ZonesⅢ. (2) The annual WUE of vegetation in the SRB exhibited a negative correlation with temperature, with large areas showing positive correlations in January-March, May, and September-December, and negative correlations in April and June-August. In contrast, the relationship between WUE and precipitation was consistently negative, with the strongest negative correlation observed in June. (3) In the SRB ecological functional Zones I-IV, the monthly WUE showed similar lagged and cumulative effects of temperature (TLA0-0) as the overall basin, while the effects of precipitation featured a three-month cumulative lag (TLA0-3).

Ecosystem

Water Use Efficiency (WUE)

Lagged effect

Temperature and precipitation

Shiyang River Basin (SRB)

Vegetation plays an important regulatory role in the water, material, and energy cycles of terrestrial ecosystems (Cramer et al., 2001; Richardson et al., 2013; Wang et al., 2020). The amount of fixed _CO2 or synthesized dry matter consumed per unit of water in vegetation photosynthesis is called water use efficiency (WUE) (Keenan et al., 2013). WUE can characterize the strength of carbon water coupling and also reflect the impact of climate change on vegetation water use strategies (Huang et al., 2015). At the ecosystem scale, WUE is usually represented by the ratio of total primary productivity (GPP) to evapotranspiration (ET) (Zhang et al., 2014), therefore, GPP and ET are key factors affecting WUE. Previous studies have shown that WUE is significantly influenced by climate factors such as temperature, precipitation, and carbon dioxide concentration, which can lead to changes of vegetation community outcomes and distribution patterns (Wang et al., 2010). The differences and specificity of the regional environment are the key factors that contribute to the spatial heterogeneity of vegetation productivity and ET (Li et al., 2023). Therefore, studying the spatial distribution patterns of WUE in different regional vegetation ecosystems is the basis for understanding water dissipation and organic carbon fixation in different vegetation ecosystems.

Precipitation and temperature are the two most studied climate factors that affect vegetation change and are crucial for plant photosynthesis and respiration (Liu et al., 2021; Liu et al., 2021; Qiao et al., 2021). Within a certain temperature range, the increase in temperature leads to a greater increase in photosynthetic carbon fixation than the increase in ET rate, resulting in an improvement in the WUE of the ecosystem. If the temperature exceeds a certain level, it will cause a decrease in the WUE of the ecosystem (Zhang et al., 2012). Precipitation change is another important aspect of climate change, and current research indicates that the response of ecosystem WUE to precipitation change depends on the water conditions of the ecosystem. The WUE of wetland and farmland ecosystems decreases with increasing precipitation, while the WUE of forest and grassland ecosystems increases with increasing precipitation (Tian et al., 2010). Due to differences in regional climate conditions, vegetation growth status, and other factors, there are differences in the spatiotemporal changes of WUE and the response to temperature and precipitation (Zhou et al., 2020). Global warming leads to frequent extreme climate events (Li et al., 2013), and extreme climate variables are more sensitive to climate change and significantly correlated with vegetation growth (Guo et al., 2022; Shao et al., 2021; Xu et al., 2019). However, the response mechanism of WUE to atmospheric temperature and precipitation in different regions is not yet clear, especially on the long-term scale. Further research is needed on the adaptive strategies and driving directions of WUE to global extreme weather change. Therefore, investigating the response relationship of WUE in different vegetation ecosystems to meteorological factors will provide a reference for rational planning of regional water resources and sustainable development of the ecological environment.

The time effect is an undeniable phenomenon in the interaction between climate and vegetation, including time delay and accumulation (Ma et al., 2022;Cun et al., 2021;Yadong et al., 2021). Climate indices, including precipitation and temperature, have a time marked effect on vegetation change (Ding et al., 2020; Zhao et al., 2021), and the time lag effect of extreme climate variables on vegetation has also been confirmed (Islam et al., 2021; Luo et al., 2020). Wu et al. (2015) found that when considering time-delay effects, climate factors can explain 64% of global vegetation changes, which is 11% higher than when ignoring time-delay effects. In terms of the cumulative effect of climate on vegetation growth, Zhang et al. (2018) pointed out that the accumulated temperature can significantly promote the growth and development of plant leaves. Evans and Geerken (2004) found that precipitation has a maximum cumulative effect on vegetation NDVI for 7 months when quantifying the impact of climate and human activities on grassland degradation in arid areas. However, the time lag or cumulative effects of climate do not exist independently, and their combined effects on vegetation growth need to be considered more comprehensively to explain the temporal effects of climate on vegetation growth (Guo et al., 2017, 2019; Ding et al., 2020).

The Shiyang River Basin (SRB) is situated in the mid-latitude Eurasian hinterland in northwestern China. The environment is fragile, with a dry climate and severe interference from human activities. It is a typical ecologically sensitive area, and the severe habitat characteristics exacerbate the contradiction between the ecological environment and socio-economic development (Wang et al., 2023; Li et al., 2021). In recent years, the climate in the SRB has significantly warmed and humidified (Zhang et al., 2019), and the atmospheric demand for water has increased, promoting the ET process of the ecosystem and leading to a decrease in WUE. The SRB is an inland river basin, and there are significant differences in the ecological environment formation foundations among different regions. Due to the influence of different climates, terrains, and water resources, different ecosystems are formed within the basin, which are strongly intervened by human activities, leading to the transformation of some natural ecosystems into artificial ecosystems. Therefore, the division of functional zones is conducive to the effective management and protection of the regional ecological environment, the utilization of regional resource advantages, the stability of basin ecosystems, and the improvement of ecological environment quality. However, the temporal and cumulative effects of WUE spatial distribution characteristics of different vegetation ecosystems in functional areas under the background of climate warming and humidification on climate factors have not been elaborated in detail. Therefore, the present study will attempt to address the following two questions by using satellite and ground-based observation data from 2001 to 2020: 1) investigate the spatiotemporal dynamic changes of WUE in the SRB; 2) clarify the response mechanism of WUE in different ecological functional areas to temperature and precipitation time effects during the process of basin humidification.

2.1 study area

The SRB is located in the transitional zone of the three major plateaus of the Loess Plateau, Qinghai Tibet Plateau, and Inner Mongolia Xinjiang Plateau, with diverse landform types and significant relative height differences; Inhabited in the hinterland of the mainland, affected by the East Asian monsoon, the South Asian monsoon and the westerly belt, the basin is dry and rainless all the year round, with sufficient sunshine, large temperature difference between day and night, strong evaporation, cold winter, hot summer, windy and dry hot wind, severe wind sand hazards, and complex climate system. It borders the northern foot of the Qilian Mountains in the south, the Tengger Desert, and Badain Jilin Desert in the north. The desert area in the basin accounts for 68%, and the total coverage area of forests, grasslands, crops, and other vegetation is only 32%. It is a typical climate-sensitive area and a fragile ecological environment zone (Li et al., 2023; Li et al., 2021).

The SRB has a large geographical span, complex and diverse landforms, and significant spatial heterogeneity of the ecosystem, resulting in significant differences in vegetation of the same type in the upper, middle, and lower reaches of the basin. (Fig. 1) Therefore, the study area is divided into ecological functional zones. Zone I (Qilian Mountains and Waters Conservation Area) This area covers the Qilian Mountains, including parts of Tianzhu, Gulang, Liangzhou, Yongchang County, Sunan, and Shandan Military Horse Farm. This area is a key area for returning farmland to forests and grasslands in the future, implementing ecological migration in the core area of water conservation forests, reducing the population size of buffer zones and their surrounding areas, and enhancing water conservation capabilities. This area establishes a sustainable water resource allocation system that conforms to the characteristics of basin resources and the laws of the market economy. It also establishes an urban oasis ecological forest network and an ecological protection forest network around the oasis to ensure ecological water use in downstream areas. Zone III (Interaction Function Zone between Desert and Oasis) covers the oasis basin north of the North Mountain in the Hexi Corridor, including Jinli District, northern Yongchang, and Minqin Basin. It is distributed discontinuously and is located in the transitional zone between the oasis and the desert downstream of the basin, directly facing the two major deserts. Zone IV (Desert Area) This area mainly refers to the desert areas surrounding the Hexi Corridor Oasis, Minqin Oasis, Changning Oasis, and Jinchuan Industrial and Mining Urban Area, including the northern mountains of the Hexi Corridor. The mainland types are deserts, Gobi, and strongly eroded low mountains(Qi et al., 2006). The precipitation in this area is sparse, and the arid desert ecosystem is composed of arid and super-arid vegetation. The vegetation of different ecosystems in the basin is significantly affected by natural resources such as climate, terrain, and water resources, resulting in differences in carbon accumulation and cycling processes (Tian et al., 2010), especially in the significant differentiation characteristics of vegetation in water use and dry matter transformation processes (Guo et al., 2019).

2.2 Data acquisition and processing

2.2.1 Remote sensing data

This study utilized the National Aeronautics and Space Administration (NASA) of the United States (http://earthdata.nasa.gov). The ET (MOD16A2GF) and GPP (MOD17A2HGF) products of the Medium Resolution Imaging Spectrometer (MODIS) have a spatial resolution of 500m, a temporal resolution of 8 days, and a period of 2000–2020. The GPP data algorithm is based on radiation utilization efficiency, and detailed calculations can be found in the literature (Running et al., 2004). The ET data algorithm is based on the Penman-Monteith equation, which comprehensively considers three processes: soil surface evaporation, canopy interception of precipitation evaporation, and plant transpiration. For detailed information, please refer to Mu et al. (2011) for batch concatenation and reprojection of MODIS data using MRT (Modis Reprojection Tool) software. Perform monthly synthesis on the reprojection data and crop out the range in the SRB. MODIS GPP and ET product data have been validated through flux tower site data in multiple regions around the world, and their accuracy has been confirmed in multiple studies.

2.2.2 Meteorological data

The annual data of temperature and precipitation in the SRB and the surrounding 8 meteorological stations from 2000 to 2020 is sourced from the Gansu Provincial Meteorological Bureau. Use ANUSPLIN meteorological data interpolation software to interpolate and obtain meteorological grid data with the same pixel size and projection as WUE data. Previous studies have shown that ANUSPLIN software has high accuracy and credibility in simulating climate data, and has been widely used in China's climate spatial simulation (Li et al., 2019).

2.3 WUE calculation

WUE refers to the amount of dry matter fixed per unit mass of water consumed by an ecosystem. This study used the commonly used method for calculating WUE in ecosystem research (Huang et al., 2015), which is the ratio of GPP to total ET (Hu et al. 2009).

$$WUE=\frac{{GPP}}{{ET}}$$

Where WUE represents the WUE of the vegetation ecosystem per unit of time (gC·kg^− 1H₂O. GPP is the GPP (gC/m^− 2) of vegetation ecosystems per unit time, based on the algorithm of light energy utilization efficiency. A detailed explanation of the calculation can be found in Running et al. (Running et al., 2004). ET is the total ET of vegetation ecosystem per unit time (mm), and the Thornthwaite ET empirical estimation model based on climate data is used to simulate the ET data of meteorological stations in the study area. The detailed calculation is explained in Pereira et al. (1989), and then the thin disc spline function interpolation method in ArcMap software is used to interpolate the ET data of meteorological stations throughout the entire study area. Related studies have shown that when simulating the ET of meteorological stations, the accuracy of the Thornthwaite empirical estimation model is high (Hafeez et al., 2020), and the complexity of the model is low, making it more suitable for simulating annual ET (Rosenberry et al., 2007).

2.4 Statistical and analysis

2.4.1 Trend Analysis

This study used univariate linear regression analysis to simulate the monthly WUE from 2000 to 2020 based on each pixel and obtained the change trend. The calculation formula is as follows:

$${\theta _{slope}}=\frac{{n \times \left( {\sum\nolimits_{{i=1}}^{n} {i \times {c_i}} } \right) - \left( {\sum\nolimits_{{i=1}}^{n} i } \right)\left( {\sum\nolimits_{{i=1}}^{n} {{c_i}} } \right)}}{{n \times \sum\nolimits_{{i=1}}^{n} {{i^2} - {{\left( {\sum\nolimits_{{i=1}}^{n} i } \right)}^2}} }}$$

Where n represents the number of years in the period (n = 21); $\:{\theta\:}_{slope}$ is the slope of the trend; $\:{C}_{i}$ for section I WUE for each month of the year. $\:{\theta\:}_{slope}$ a negative value indicates a downward trend in WUE, $\:{\theta\:}_{slope}$ a positive value indicates an upward trend in WUE.

2.4.2 Correlation analysis

Pearson correlation analysis was used to calculate the correlation coefficients between monthly WUE, temperature, and precipitation on a pixel-by-pixel basis, using years as time units. The calculation formula is as follows:

$${R_{xy}}=\frac{{\sum\nolimits_{{i=1}}^{n} {\left[ {\left( {{x_i} - \overline {x} } \right)\left( {{y_i} - \overline {y} } \right)} \right]} }}{{\sqrt {\sum\nolimits_{{i=1}}^{n} {\left( {{x_i} - \overline {x} } \right)} } \sum\nolimits_{{n=i}}^{n} {\left( {{y_i} - \overline {y} } \right)} }}$$

Where $\:{R}_{xy}$ represents the correlation coefficient between two variables; $\:{x}_{i}$ represents the annual GPP/ET/WUE of each month; $\:{y}_{i}$ represents the temperature and precipitation in the I year of each month; $\:\overline{x}$ represents the average annual GPP/ET/WUE for each month; $\:\overline{y}$ represents the average annual temperature and precipitation for each month; I represents the number of samples. $\:{R}_{xy}$is the correlation between GPP/ET/WUE and temperature and precipitation; When $\:{R}_{xy}$ is positive, it indicates a positive correlation between GPP/ET/WUE and temperature and precipitation, and when negative, it indicates a negative correlation between GPP/ET/WUE and temperature and precipitation.

2.4.3 Significance test

The significance represents the level of confidence in the trend of change and the correlation coefficient, and the calculation formula is as follows:

$$F=U \times \frac{{n - 2}}{Q}$$

Where $\:U=\sum\:_{i=1}^{n}{{(y}_{i}-\overline{y})}^{2}$ is the sum of squares for regression; $\:Q=\sum\:_{i=1}^{n}{{(y}_{i}-\overline{y})}^{2}$ is the sum of squared residuals;$\:{y}_{i}\:is\:tℎe\:WUE\:value\:for\:tℎe$ $\:year\:of\:eacℎ\:montℎ$; $\:{y}_{i}$ is the regression value; $\:\overline{y}$ is the average annual WUE for each month, and n=20 is the number of years. For the spatiotemporal trend of WUE in each month, based on the F-test results, the following six levels of change are obtained: extremely significant decrease, significant decrease, insignificant decrease, insignificant increase, significant increase, and extremely significant increase. The correlation between WUE and temperature/precipitation, according to the F-test results, is divided into the following six levels of change: extremely significant decrease, significant decrease, insignificant decrease, insignificant increase, significant increase, and extremely significant increase.

2.5 Lag Effect of Temperature and Precipitation on WUE

Select the monthly scale data of vegetation temperature, precipitation, and WUE in the watershed from 2001 to 2020. Combine the monthly temperature and precipitation with the WUE monthly data of the current month and the previous i months to form a series (0 ≤ i ≤ 12). Next, calculate the R for the lag time scale of i months and obtain 12 correlation coefficients for each pixel (Formula (5)). For example, with a lag scale of 3 months, a correlation analysis can be conducted between the monthly temperature and precipitation data from January to December 2001–2020 and WUE on a monthly basis, and so on, up to a lag of 12 months.

Select the maximum correlation coefficient Rmax_lag and consider the corresponding lag month as the size and time scale of the lag effect of the pixel (Formula (6)). For instance, if the Rmax_lag lag occurs in March between the current month's temperature and precipitation and the WUE of 1 month, the lag response time scale of vegetation temperature and precipitation to WUE is recorded as 3 months, indicating that the WUE of 3 months ago has a key impact on the changes in vegetation WUE.

$${R_i}=corr\left( {WUE,prep/temp} \right)1 \leqslant i \leqslant 12$$

$${R_{\hbox{max} \_lag}}=\hbox{max} \left( {{R_i}} \right)1 \leqslant i \leqslant 3$$

Where WUE represents the monthly WUE time series from 2001 to 2020 with a lag of i months. Temperature/precipitation is the 1-month temperature and precipitation time series with a lag of i months, and R_i is the Pearson correlation coefficient with a lag of i months.

2.6 Cumulative effect of temperature and precipitation on WUE

To investigate the cumulative impact of temperature and precipitation on WUE, the Pearson correlation coefficient (Pearson) between monthly WUE and cumulative temperature/precipitation was used to obtain the time scale corresponding to the maximum correlation of temperature and precipitation (Zhao et al., 2020, Peng et al., 2019). Firstly, associate the WUE time series with the temperature and precipitation time series of m months (1 ≤ m ≤ 12) and calculate R as formula (7). Then, the accumulated months of temperature/precipitation for Rmax_cum, which has the greatest correlation with WUE, are considered as the time scale formula for the cumulative effect (8), and Rmax_cum is determined as the cumulative effect quantity. For example, if the correlation between monthly WUE and 3 months of temperature/precipitation is the greatest, the time scale for the cumulative effect is recorded as 3 months, indicating that the accumulated 3 months of temperature/precipitation before the current month have the greatest impact on vegetation photosynthesis.

$${R_m}=corr\left( {WUE,mprep/mtemp} \right)1 \leqslant m \leqslant 12$$

$${R_{\hbox{max} \_cum}}=\hbox{max} \left( {{R_m}} \right)1 \leqslant m \leqslant 3$$

Where ཌྷ is the cumulative time scale of temperature/precipitation data, map/mtemp is a temperature/precipitation time series with m cumulative months, and R_m is the Pearson correlation coefficient between WUE and temperature/precipitation.

3.1 Temporal–spatial of WUE in SRB

3.1.1 Spatial patterns of WUE in SRB

In the past 20 years, the annual average WUE in the SRB has been 0.99 gC·kg^− 1 H₂O, and the spatial distribution generally shows a characteristic of high in the south and low in the north (Fig. 2a). The annual WUE in the basin is higher than 1.2 gC·kg^− 1 H₂O, mainly distributed in the southern part of Zone I, Zone II, and Zone III of Changning Oasis, as well as the Jinchuan and Hexibao Oasis areas, accounting for 27.87% of the total vegetation area in the basin (Fig. 2b). The annual WUE is below 0.4 gC·kg^− 1 H₂O, mainly distributed near the Qilian Mountains cold desert area in Zone I, accounting for 3.87% of the total vegetation area in the basin. Because most of Zone IV is located in desert areas with low vegetation coverage, the overall annual average WUE in the region is the lowest, at 0.80 gC·kg^− 1 H₂O.

3.1.2 Temporal–spatial trends of WUE in SRB

During the period from 2001 to 2020, the area of vegetation with WUE higher than 1.2 gC·kg^− 1 H₂O in the SRB gradually decreased year by year, indicating a decreasing trend in WUE in the SRB, with the decreasing area accounting for 79.05% of the total vegetation area in the basin. In 2002, the annual mean WUE reached the highest value in nearly 20 years, at 1.17 gC·kg^− 1 H₂O (Fig. 2d). The overall rate of WUE change in the SRB is -0.0056 gC·kg^− 1 H₂O · yr^− 1 year by year. The annual WUE change in the basin mainly shows a nonsignificant decrease (Fig. 2c), with the nonsignificant decrease area accounting for 35.43% of the total area in the basin, followed by a very significant decrease, accounting for 29.08% of the area. Comparing the annual WUE changes of different ecological functional zones in the SRB, all ecological functional zones show a downward trend, with Zone III showing the most significant downward trend, accounting for 89.85% of the total vegetation area in Zone III; The proportion of WUE increasing year by year in Zone II is the highest in Zone 4, at 25.32% (Fig. 2e).

Based on the monthly changes of WUE within the year, it can be seen that the WUE in the SRB showed an upward trend in February and November December (Fig. 2f), and the upward trend was most significant in December, with the WUE rising area accounting for 63.40% of the total area, of which the extremely significant rising area accounted for 41.44% of the total area; The WUE in January and March October showed a decreasing trend month by month, with June showing the most significant decline. The area of WUE decline accounted for 92.20% of the total area, with a significant decrease accounting for 35.84% of the total area and a highly significant decrease accounting for 35.63% of the total area.

3.2 Correlation between temperature/precipitation and WUE

3.2.1 Correlation between temperature and WUE

From 2001 to 2020, the overall vegetation in the SRB showed a negative correlation between WUE and temperature year by year (Fig. 3), accounting for 96% of the total basin area. Among them, 58.1% were mainly non-significantly negatively correlated, distributed in Zone I and Zone II, accounting for 31.5% and 17.8% of the total basin area, respectively; The areas with significant negative correlation and extremely significant negative correlation are mainly distributed in the northeast of Zone I, accounting for 17% of the total basin area. The area with a positive correlation between WUE and temperature each year is 3.16%, mainly distributed in the southwest of Zone I and the central corridor of Zone II. The regions with insignificant positive correlation and significant positive correlation are mainly distributed in Zone I, accounting for 2.9% of the total basin area. The correlation between annual WUE and precipitation in Zone III (R=-0.5191) is stronger than that in Zone I (R=-0.5163), Zone II (R=-0.4170), and Zone IV (R=-0.5185), indicating that vegetation in Zone III responds more strongly to temperature year by year.

Over the past 20 years, the monthly WUE in the SRB has shown a large positive correlation with temperature in the study area from January to March, May, and September to December, with a large negative correlation observed in April and June to August; The negative correlation between WUE and temperature from June to July is mainly distributed in Zone I (Fig. 4). From September to March of the following year, the correlation between WUE and temperature in the SRB showed a positive correlation overall, with the most significant positive correlation from December to February of the following year. Among them, the proportion of highly significant positive correlation area in December was the highest in all months of the year, accounting for 98.58% of the vegetation coverage area in the basin. In April, the monthly WUE showed a nonsignificant negative correlation with temperature, and the nonsignificant negative correlation area accounted for 51.93% of the vegetation coverage area in the basin. From May to August, as time goes by, the correlation between vegetation monthly WUE and temperature gradually changes from positive to negative. Among them, the negative correlation between vegetation monthly WUE and temperature in July is the highest throughout the year, with the negative correlation area accounting for 92.66% of the vegetation coverage area in the basin, and the insignificant negative correlation accounting for 83.54%. Comparing the monthly correlation between WUE and temperature in different ecological functional zones on the SRB, it was found that the correlation changes in the I-IV region were positively correlated with the overall SRB from September to March of the following year. From May to August, the correlation gradually changed from positive to negative over time. In April, due to the large area of insignificant negative correlation in the I region, the monthly WUE in the SRB showed a negative correlation with the overall temperature; In addition, due to the influence of temperature from December to February of the following year, the vegetation coverage in the southeast of Zone I is low, resulting in a correlation of WUE and temperature in the southeast of Zone I being almost zero.

3.2.2 Correlation between precipitation and WUE

From 2001 to 2020, the overall vegetation in the SRB showed a negative correlation between WUE and precipitation year by year (Fig. 5), accounting for 94% of the total basin area. Among them, the area mainly showed a nonsignificant negative correlation was 71%, distributed in Zone I and Zone II, accounting for 31% and 17.6% of the total basin area, respectively. The areas with significant negative correlation and extremely significant negative correlation are mainly distributed in Zone I, accounting for 15% of the total basin area. The area of WUE positively correlated with precipitation year by year is 6.35%, mainly distributed in Zone I and Zone II; The areas with insignificant positive correlation and significant positive correlation are mainly distributed in the southwest of Zone I, accounting for 5.4% of the total basin area. The correlation between WUE and precipitation in Zone II is stronger year by year (R=-0.5199) than in Zone I (R=-0.0350), Zone III (R=-0.4435), and Zone IV (R=-0.1579), indicating that vegetation in Zone II is more significantly affected by precipitation.

The correlation between monthly WUE and precipitation in the SRB shows a negative correlation throughout the year (Fig. 6), which is different from the correlation with temperature. The negative correlation is most significant in June, with significant negative correlation and extremely significant negative correlation accounting for 67.91% of the vegetation coverage area in the basin. From June to mid-August, all ecological functional zones showed a negative correlation, but over time, except for Zone I, all other three zones showed a large positive correlation; In June, there was a large positive correlation in the central part of Zone II, accounting for 13.75% of the total vegetation area in Zone II; Appearing in the northwest of Zone IV in July, accounting for 32.42%; In August, it appeared in the Minqin Oasis, accounting for 17.50%. In September, the proportion of positively correlated areas in Zone I was 18.75%, and in Zone II it was 11.74%. From September to May of the following year, the regionalization of various ecological functions showed a negative correlation, but both Zone I and Zone II showed a large positive correlation in November, with 19.63% in Zone I and 9.78% in Zone II.

3.2.3 Effect of lag and cumulative on WUE

The spatial distribution pattern of WUE and temperature for different lag and cumulative months in the SRB (Fig. 7a), whilehows the proportion of lag and cumulative months in different ecological function zones (Fig. 7c) s. The main correlation between monthly WUE and temperature lag and cumulative months is TLA0-0 (0-month lag and 0-month accumulation), accounting for 91.59% of the vegetation coverage area in the basin. The lag and cumulative effects of monthly WUE and temperature in the I-IV region are similar to the overall basin, both showing TLA0-0 (0-month lag and 0-month accumulation), and the area proportion of TLA0-0 (0-month lag and 0-month accumulation) in the II-IV region is higher than 99%. However, there is a large area of TLA0-3 (0-month lag and 3 months accumulation) near the Qilian Mountains cold desert area in the I region. This indicates that the lag and cumulative effect of temperature on the monthly WUE in the SRB is weak, but the cumulative effect of temperature on the monthly WUE in the Qilian Mountains cold desert area of Zone I is significant.

The overall spatial distribution of monthly WUE and precipitation in the SRB shows TLA0-3 (0-month lag and 3-month accumulation) with different lag and cumulative months (Fig. 7b). The spatial distribution of precipitation lag and cumulative months in various ecological functional zones of Shiyang River is consistent with the overall basin, showing TLA0-3 (0-month lag and 3-month accumulation), and the area proportion of TLA0-3 in II-IV zone is above 95% (Fig. 7d). However, a large area of TLA2-1 (2-month lag and 1-month accumulation) appears in the southwest of Zone I. This indicates that the monthly WUE in the SRB is mainly affected by the 3-month cumulative effect of precipitation, while the monthly WUE in the southwest of Zone I is mainly affected by the combination of the lag and cumulative effects of precipitation.

4.1 Characteristic of Ecological Function Area WUE from 2001 to 2020

The spatial distribution characteristics of WUE represent the characteristics of water use in different regions and vegetation types of ecosystems, as well as the adaptation results of human intervention processes (Sullivan et al., 2005). The SRB is located in the northwest arid region, with fragile habitats, a dry climate, and significant human disturbance. It belongs to a typical ecologically sensitive area (Chen et al., 2014; Feng et al., 2012). Zone I can gather natural precipitation and conserve water sources, providing a habitat for high-altitude organisms in mid-latitudes. The southern part of the area is mainly composed of high-coverage grasslands and tall trees, with a balanced and stable distribution of rivers. The soil moisture content is high, providing a foundation for high carbon sequestration of vegetation. The diverse vegetation types and abundant water sources are important reasons for the high annual WUE in the area. Providing a foundation for the high carbon sequestration value of vegetation, the diverse vegetation types and abundant water sources are important reasons for the high annual WUE in the region. The northern region is mainly composed of low-cover grasslands and exposed wasteland, with unstable distribution of groundwater and surface runoff, sparse and single vegetation types, which is the main reason for the low annual WUE in the region. Zone II is the main oasis in the SRB, with abundant groundwater resources that can provide water for vegetation growth. After 2000, large-scale ecological governance projects were carried out in the Hexi Corridor region. After 20 years of continuous efforts, progress has been made in areas such as sand prevention and control, watershed ecological protection, and vegetation restoration (Zhao et al., 2021). The increase in vegetation coverage leads to a higher average annual WUE in this area. Zone III is located in the transitional zone between oasis and desert in the lower reaches of the watershed, directly facing the two major deserts. Within the region, there are Minqin Oasis, Changning Oasis, Jinchuan Oasis, and Hexibao Oasis. The area lacks water and is affected by drought, making it difficult for vegetation to sequester carbon. At the same time, the desert terrain makes it difficult to retain water, resulting in increased water evaporation. The overall average annual WUE value is relatively low, but the oasis area is higher than other areas in the same area. The precipitation in Zone IV is sparse, and the arid desert ecosystem is composed of arid and ultra-arid vegetation. The vegetation is mostly low and sparse, and the groundwater system is relatively underdeveloped. Therefore, the overall average annual WUE value of vegetation is the lowest in the SRB.

The spatial distribution of WUE characterizes the differences in WUE among different ecological functional zones, while the temporal variation of WUE well presents the results of vegetation's changes in water consumption and productivity in the face of environmental factors and human intervention. From 2001 to 2020, the overall WUE in the SRB showed a decreasing trend year by year, mainly due to the trend of warming and humidifying the overall climate in the northwest arid region in recent years, as well as the recent sand fixation and afforestation plans (ecological governance plans) in the SRB. This has led to an increase in vegetation coverage, enhanced carbon sequestration capacity, and enhanced water retention capacity in the SRB, especially in Zone II. The ecosystem environment has been greatly improved, increasing WUE. The vegetation in Zone III has shown the most significant decline in WUE year by year, possibly due to urban expansion in Liangzhou District, where the land cover type has changed from arable land to urban land, resulting in a decrease in vegetation coverage in this area; After 2010, the Minqin Oasis implemented agricultural industry structure adjustment, and some areas changed their crop types from traditional crops to economic crops, increasing the construction area of agricultural greenhouses. Therefore, the vegetation in the area may deteriorate, leading to unstable carbon dioxide fixation capacity and weakened ET.

4.2 Interactions between temperature/precipitation and WUE

There are significant spatial differences and seasonal variations in the response of WUE to water and heat factors in different ecological functional zones. From 2001 to 2020, the WUE in the SRB showed a negative correlation with temperature and precipitation year by year. The sensitivity of WUE to precipitation in Zone I and IV was lower than that of temperature. The correlation between vegetation growth and temperature was higher in the high-altitude grassland area of Zone I; Zone IV is mainly dominated by arid vegetation, with a large temperature difference between day and night in the desert and Gobi. Based on the existing vegetation, water is no longer a limiting condition, and temperature has become an important factor limiting vegetation. However, the vegetation in Zone II is increasingly sensitive to precipitation because the land use type in the area is mainly cultivated land. During the cultivation process, land management is adjusted according to demand, especially the use of irrigation technology, which directly disturbs the correlation between WUE and climate. Therefore, the annual WUE in different ecosystems in the SRB is limited by temperature but is influenced by factors such as human activities, land use patterns, and management measures. The WUE in the SRB has seasonal characteristics. From December to February of the following year, the overall climate of the SRB is cold. Due to the replenishment of groundwater for vegetation growth, heat becomes the main limiting factor for vegetation growth. Therefore, the monthly WUE is positively correlated with temperature, but not significantly negatively correlated with precipitation. During the spring flood period from March to May, Zone I has a high altitude and most areas are located at high altitudes. The melting of ice and snow increases the replenishment of soil moisture, while the rise in temperature increases the stomatal conductance of vegetation, which is beneficial for vegetation growth. However, vegetation is in the developmental stage and has weak carbon sequestration ability. Therefore, there is a large negative correlation between WUE and temperature in April. Zone II and Zone III are densely populated areas in the basin, and vegetation is greatly affected by human activities. Because some of the above-ground runoff is used for irrigation in the central agricultural area, while the underground water infiltrated is extracted for domestic use, there are very few water resources available for ecological use. It can be seen that vegetation located in desert areas and oasis desert transition zones has a low utilization rate of above-ground runoff, and monthly WUE shows a significant negative correlation with precipitation. From June to August, as the temperature increases, the stomatal conductance and transpiration of vegetation in zones I, II, and III decrease (Hu et al., 2009). However, at this time, vegetation is in the growth stage, and the fixed organic matter content that consumes a unit area of water increases. Monthly WUE is positively correlated with temperature. As the temperature rises in Zone I, the southern ice, and snow melt water supply is sufficient, the soil moisture content increases and vegetation growth is not affected by water stress. The monthly WUE is significantly negatively correlated with precipitation. From September to November, vegetation is in the late growing season, and a decrease in temperature can lead to a decrease in the stomatal conductance of vegetation leaves. Plants can use less water to exchange for an equal amount of carbon, and the physiological effect of _CO2 enrichment has a greater alleviating effect on vegetation water stress than the exacerbation of atmospheric water deficit, thereby significantly reducing the water demand of the ecosystem and improving plant WUE. Therefore, monthly WUE is positively correlated with temperature (Li et al., 2018; Lian et al., 2021). The altitude of Zone I is mostly above 3000m. Due to sparse human habitation, WUE has a higher response to climate factors every month. As the temperature begins to decrease, melting water from ice and snow decreases, and soil water supply decreases. Therefore, monthly WUE is positively correlated with precipitation.

The temporal effect of climate on vegetation growth varies with climate factors and regions, and these research results are consistent with Ding et al. (2020) findings at the global scale. The lagged response of monthly WUE in the SRB to temperature and precipitation is not significant, but the cumulative response is significant. The main combination of temperature and precipitation lag and accumulation months is TLA0-0 (0 months lag 0 months accumulation) and TLA0-3 (0 months lag 3 months accumulation), which reflects the rapid response of vegetation to climate factors. The response of vegetation in Zone I to temperature every month is mainly characterized by a lag of 1 months and a cumulative effect of 2 months. This may be due to the high sea level and year-round cold temperatures in the Qilian Mountains. At the same time, most areas are mainly composed of high mountain wasteland, distributed with alpine meadows, with a relatively single vegetation type and strong adaptability to low-temperature environments. The response of vegetation to the lag and cumulative effects of precipitation on a monthly basis is relatively strong at 0 month lag and 3 months accumulation. The study area is located in the arid region of northwest China, with year-round drought little rainfall, and a poor climate environment. Precipitation is one of the main influencing factors of the vegetation ecosystem in the SRB, so vegetation responds quickly to precipitation. The occurrence of a 3-month accumulation may be due to the need for plant growth and development to accumulate a certain amount of temperature and water to differentiate, germinate, and bloom (Chen et al., 2014; Ding et al., 2020). There has been significant progress in the ecological environment management on the SRB in Zone II, III, and IV, including the artificial water conveyance of Qingtu Lake and the continuous management of the Minqin Oasis area. These have provided better water conditions for plants, stabilized the ecosystem, and enhanced their resistance to short-term drought. Therefore, the cumulative effect of WUE on precipitation every month is manifested as a 3-month accumulation.

During the past 20 years, the average annual WUE value of vegetation in the SRB has been 0.99 gC·kg^− 1 H₂O, and the spatial distribution generally shows a high in the south and low in the north. The oasis areas in the southern part of Zone I, Zone II, and Zone III have higher WUE values year by year, while the Qilian Mountains cold desert area in Zone I and the desert area in Zone IV have lower WUE values. From 2001 to 2020, WUE values showed a decreasing trend year by year, with the average reaching the lowest point in 2011 and the highest point in 2002; By studying the monthly variation of WUE between different months, WUE showed an upward trend in February and November December, while WUE showed a downward trend in January and March October. The downward trend is most significant in Zone III of the SRB, and the proportion of the rising area in Zone II is the largest.

The correlation between WUE and temperature of vegetation in the SRB from May to September, as well as different ecological functional zones, showed a fluctuating pattern from 2001 to 2020. There was a positive correlation from May to June and a negative correlation from July to August; The monthly WUE in the SRB is positively correlated with temperature in the study area from January to March, May, and September to December, with a large negative correlation observed in April and June to August; The negative correlation between WUE and temperature from June to July is mainly distributed in Zone I, and the positive correlation is most significant in December. Except for Zone I, which showed a large negative correlation in April, the correlation changes in various ecological functional areas are positively correlated with the overall SRB; Compared with temperature, the correlation between monthly WUE and precipitation in the SRB is generally negative, with the most significant negative correlation in June. All ecological functional areas show a negative correlation, and with time changes, except for Zone I, the other three areas show a large positive correlation. At the same time, Zone I and Zone II also show a large positive correlation in November.

The monthly WUE in the SRB is mainly affected by temperature lag and accumulation months, with TLA0-0 (0-month lag and 0-month accumulation). The WUE and temperature lag and accumulation months in the I-IV area are the same as the overall basin, but there is a large area of TLA0-3 (0-month lag and 3 months accumulation) near the Qilian Mountains cold desert area in the I area; The spatial distribution of WUE, which differs from precipitation, shows an overall TLA0-3 (0 months followed by 3 months of accumulation). However, in the southwestern part of Zone I, there is a partial TLA2-1 (2 months followed by 1-month accumulation), and WUE is mainly influenced by the combination of precipitation lag and accumulation effects.

Acknowledgments

This research was jointly supported by the Yunnan Provincial Basic Research (202301AT070084 and 202301AT070085),Western Yunnan University of Applied Sciences Talent Introduction Research Initiation Project (2023RCKY0001 and 2022RCKY0003)。

Cramer, W., Bondeau, A., Woodward, F. I., Prentice, I. C., Betts, R. A., & Brovkin, V., et al., 2001. Global responces of terrestrial ecosystem structure and function to CO2 and climate change: results from six dynamic global vegetation models. Global Change Biology, 4, 357-373.
Chen Y , Li Z , Fan Y ,et al.Research progress on the impact of climate change on water resources in the arid region of Northwest China[J].Fresenius Environmental Bulletin, 2014, 69(9):1295-1304.
Chen C, Kang Y, Huo Z, et al. Highly crystalline multimetallic nanoframes with three-dimensional electrocatalytic surfaces. Science (New York, N.Y.). 2014 Mar;343(6177):1339-1343.
Cun Zhan, Chuan Liang, Lu Zhao, etal.,2022.Drought-related cumulative and time-lag effects on vegetation dynamics across the Yellow River Basin, China,Ecological Indicators,143,109409.
Ding, Y., Li, Z., & Peng, S., 2020. Global analysis of time-lag and -accumulation effects of climate on vegetation growth. International Journal of Applied Earth Observation and Geoinformation, 92, 102179.
Ding, D., Cruz, B. D. P., Green, M. A., & Bauman, A. E., 2020. Is the covid-19 lockdown nudging people to be more active: a big data analysis. British journal of sports medicine, 54(20), 1183-1184.
Evans, J., & Geerken, R., 2004. Discrimination between climate and human-induced dryland degradation. Journal of Arid Environments, 57, 535-554.
Feng, Q., Li, Z., Gao, Q., & Si, J., 2012. Ecosystem Water Needs and Ecosystem Rehalibitation of Minqin Oasis in Shiyang River Basin. Advances in Earth Science, 27(7), 806.
Guo L, Cheng J, Luedeling E, et al. Critical climate periods for grassland productivity on China’s Loess Plateau[J]. Agricultural and Forest Meteorology, 2017, 233: 101-109.
Guo L, Wang J, Li M, et al. Distribution margins as natural laboratories to infer species’ flowering responses to climate warming and implications for frost risk[J]. Agricultural and Forest Meteorology, 2019, 268: 299-307.
Guo, Z., Lou, W., Sun, C., & He, B., 2022. Trend changes of the vegetation activity in northeastern East Asia and the connections with extreme climate indices. Remote Sensing, 14(13), 3151.
Hu, Z. M., Yu, G. R., Wang, Q. F., & Zhao, F. H., 2009. Ecosystem level water use efficiency: A review. Acta Ecologica Sinica, 29(3), 1498-1507.
Huang, M., Piao, S., Sun, Y., Ciais, P., Cheng, L., Mao, J., ... & Wang, Y., 2015. Change in terrestrial ecosystem water‐use efficiency over the last three decades. Global Change Biology, 21(6), 2366-2378.
Hafeez, M., Bakhsh, A., Basit, A., Chattha, Z. A., & Tahira, F., 2020. Penman and thornwait equations for estimating reference ET under semi-arid environment. Current Research in Agricultural Sciences, 7, 6-14.
Islam, A. R. M. T., Islam, H. T., Shahid, S., Khatun, M. K., Ali, M. M., Rahman, M. S., ... & Almoajel, A. M., 2021. Spatiotemporal nexus between vegetation change and extreme climatic indices and their possible causes of change. Journal of Environmental Management, 289, 112505.
Keenan, T. F., Hollinger, D. Y., Bohrer, G., Dragoni, D., Munger, J. W., Schmid, H. P., & Richardson, A. D., 2013. Increase in forest water-use efficiency as atmospheric carbon dioxide concentrations rise. Nature, 499(7458), 324-327.
Li, Y., Wang, J., Li, Y., 2013. Comparative study of the two methods for identifying monthly drought process in the Yellow River basin. Journal of Glaciology and Geocryology, 35(4), 968—977.
Li, L., 2018. The correlationship between permafrost micro-environment and vegetation succession in upper reaches of Heihe. Lanzhou University.
Li, C., Wang, Y., Wu, X., Cao, H., Li, W., & Wu, T., 2021. Reducing human activity promotes environmental restoration in arid and semi-arid regions: A case study in Northwest China. Science of the Total Environment, 768, 144525.
Li, L., Du, J., Li, Y., Chen, M., Wan, B., Wang, D., 2023. Changes of vegetation water use efficiency and their responding to scPDSl in frozen ground area of the Qilian Mountains from 2000 to 2020[J]. Journal of Glaciology and Geocryology, 45(2), 683-698.
Lian, X., Piao, S., Chen, A., Huntingford, C., Fu, B., Li, L. Z., ... & Roderick, M. L., 2021. Multifaceted characteristics of dryland aridity changes in a warming world. Nature Reviews Earth & Environment, 2(4), 232-250.
Liu, H., Deng, Y., & Liu, X., 2021. The contribution of forest and grassland change was greater than that of cropland in human-induced vegetation greening in China, especially in regions with high climate variability. Science of the Total Environment, 792, 148408.
Liu, H., Zhang, A., Liu, C., Zhao, Y., Zhao, A., & Wang, D., 2021. Analysis of the time-lag effects of climate factors on grassland productivity in Inner Mongolia. Global Ecology and Conservation, 30, e01751.
Luo, M., Sa, C., Meng, F., Duan, Y., Liu, T., & Bao, Y., 2020. Assessing extreme climatic changes on a monthly scale and their implications for vegetation in Central Asia. Journal of Cleaner Production, 271, 122396.
Ma, Y., Guan, Q., Sun, Y., Zhang, J., Yang, L., Yang, E., ... & Du, Q., 2022. Three-dimensional dynamic characteristics of vegetation and its response to climatic factors in the Qilian Mountains. Catena, 208, 105694.
Mu, Q., Zhao, M., & Running, S. W., 2011. Improvements to a MODIS global terrestrial ET algorithm. Remote sensing of environment, 115(8), 1781-1800.
Pereira, A. R., & De Camargo, Â. P., 1989. An analysis of the criticism of Thornthwaite's equation for estimating potential evapotranspiration. Agricultural and Forest Meteorology, 46(1-2), 149-157.
Peng, J., Wu, C., Zhang, X., Wang, X., & Gonsamo, A., 2019. Satellite detection of cumulative and lagged effects of drought on autumn leaf senescence over the Northern Hemisphere. Global Change Biology, 25(6), 2174-2188.
Qi, Y., Li, J., Zhang, J., Niu, S., Xu, X., 2006. Research on the ecological function area of Shiyang River Basin. Journal of Lanzhou University: Natural Science Edition, 42(4), 29-33.
Qiao, Y., Jiang, Y., & Zhang, C., 2021. Contribution of karst ecological restoration engineering to vegetation greening in southwest China during recent decade. Ecological Indicators, 121, 107081.
Renjun Li , Maofang G , Qiang L ,et al.Research on rainfall spatial interpolation method based on ANUSPLIN[J].China Agricultural Informatics, 2019.
Rosenberry, D. O., Winter, T. C., Buso, D. C., & Likens, G. E,. 2007. Comparison of 15 evaporation methods applied to a small mountain lake in the northeastern USA. Journal of hydrology, 340(3-4), 149-166.
Richardson, A. D., Keenan, T. F., Migliavacca, M., Ryu, Y., Sonnentag, O., & Toomey, M., 2013. Climate change, phenology, and phenological control of vegetation feedbacks to the climate system. Agricultural and Forest Meteorology, 169, 156-173.
Running, S. W., Nemani, R. R., Heinsch, F. A., Zhao, M., Reeves, M., & Hashimoto, H., 2004. A continuous satellite-derived measure of global terrestrial primary production. Bioscience, 54(6), 547-560.
Shao, H., Zhang, Y., Gu, F., Shi, C., Miao, N., & Liu, S., 2021. Impacts of climate extremes on ecosystem metrics in southwest China. Science of the Total Environment, 776, 145979.
Sullivan, P. F., & Welker, J. M., 2005. Warming chambers stimulate early season growth of an arctic sedge: results of a minirhizotron field study. Oecologia, 142, 616-626.
Tian, H., Chen, G., Liu, M., Zhang, C., Sun, G., Lu, C., ... & Chappelka, A., 2010. Model estimates of net primary productivity, evapotranspiration, and water use efficiency in the terrestrial ecosystems of the southern United States during 1895–2007. Forest ecology and management, 259(7), 1311-1327.
Wang, Q., Yu, D., Dai, L., Zhou, L., Zhou, W., Qi, G., ... & Ye, Y., 2010. Research progress in water use efficiency of plants under global climate change. Yingyong Shengtai Xuebao, 21(12), 11.
Wang, L., Li, M., Wang, J., Li, X., & Wang, L., 2020. An analytical reductionist framework to separate the effects of climate change and human activities on variation in water use efficiency. Science of the total environment, 727, 138306.
Wang, D., Zhao, C., Fang, F., Lin, J., Li, L.,Yang, Y., 2023. Spatial-temporal dynamics of water use efficiency and responding to vapor pressure deficit in Shiyang River Basin, northwestern China. Acta Ecologica Sinica, 43(8), 3090-3102.
Wu, D., Zhao, X., Liang, S., Zhou, T., Huang, K., Tang, B., & Zhao, W., 2015. Time‐lag effects of global vegetation responses to climate change. Global change biology, 21(9), 3520-3531.
Xu, C., McDowell, N. G., Fisher, R. A., Wei, L., Sevanto, S., Christoffersen, B. O., ... & Middleton, R. S., 2019. Increasing impacts of extreme droughts on vegetation productivity under climate change. Nature Climate Change, 9(12), 948-953.
Yadong Ji, Yi Li, Ning Yao, etal.,2021.The lagged effect and impact of soil moisture drought on terrestrial ecosystem water use efficiency, Ecological Indicators,133,108349.
Zhao, A., Yu, Q., Feng, L., Zhang, A., & Pei, T., 2020. Evaluating the cumulative and time-lag effects of drought on grassland vegetation: A case study in the Chinese Loess Plateau. Journal of environmental management, 261, 110214.
Zhao, Q., Zhang, J., Zhao, T., & Li, J., 2021. Vegetation changes and its response to climate change in China Since 2000. Plateau Meteorol, 40, 292-301.
Zhang, Z., Jiang, H., Liu, J. X., Zhou, G. M., Liu, S. R., & Zhang, X. Y., 2012. Assessment on water use efficiency under climate change and heterogeneous carbon dioxide in China terrestrial ecosystems. Procedia Environmental Sciences, 13, 2031-2044.
Zhang, L., Hu, Z., Fan, J., Shao, Q., Tang, F., 2014. Advances in the spatiotemporal dynamics in ecosystem water use efficiency at regional scale. Advances in Earth Science, 29(6) , 691-699.
Zhang, H., Liu, S., Regnier, P., & Yuan, W., 2018. New insights on plant phenological response to temperature revealed from long‐term widespread observations in China. Global change biology, 24(5), 2066-2078.
Zhang, Q., Lin, J., Liu, W., & Han, L., 2019. Precipitation seesaw phenomenon and its formation mechanism in the eastern and western parts of Northwest China during the flood season. Science China Earth Sciences, 62(12), 2083-2098.
Zhou, X., Sun, P., Zhang, M., & Liu, S., 2020. Spatio-temporal characteristics of vegetation water use efficiency and their relationships with climatic factors in alpine and subalpine area of southwestern China. Chinese Journal of Plant Ecology, 44(6), 628

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The lag and cumulative response of water use efficiency to the climate on the Shiyang River Basin, Northwest of China

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Abstract

Figures

1. Introduction

2. Materials and methods

2.1 study area

2.2 Data acquisition and processing

2.2.1 Remote sensing data

2.2.2 Meteorological data

2.3 WUE calculation

2.4 Statistical and analysis

2.4.1 Trend Analysis

2.4.2 Correlation analysis

2.4.3 Significance test

2.5 Lag Effect of Temperature and Precipitation on WUE

2.6 Cumulative effect of temperature and precipitation on WUE

3. Results

3.1 Temporal–spatial of WUE in SRB

3.1.1 Spatial patterns of WUE in SRB

3.1.2 Temporal–spatial trends of WUE in SRB

3.2 Correlation between temperature/precipitation and WUE

3.2.1 Correlation between temperature and WUE

3.2.2 Correlation between precipitation and WUE

3.2.3 Effect of lag and cumulative on WUE

4. Discussion

4.1 Characteristic of Ecological Function Area WUE from 2001 to 2020

4.2 Interactions between temperature/precipitation and WUE

5. Conclusions

Declarations

References

Additional Declarations

Supplementary Files

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